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Machine Learning for retail and ecommerce

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Andrei Lopatenko
Andrei Lopatenko

How Machine Learning can drive ecommerce and retail revenues

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Machine Learning (ML) for eCommerce and Retail Dr. Andrei Lopatenko Director of Engineering, Recruit Institute of Technology Recruit Holdings former Walmart Labs, Google (twice), Apple (twice) andrei@recruit.ai
ML for eCommerce • Search, Browse, for commerce sites and application • Help users to find and discover items they will purchase • Maximize revenue/profit per user session
Search
Search - ranking ranking
Search - LHN Left Hand Navigation
Search spell correction
Search type ahead
Browse
Search data size • Catalogue items • 8 M items now compare ~ 400 M Amazon / eBay • X 10 in near future • 2 K text description per item + images • Several hundreds of structured attributes per catalog
Search – user searches • Tens of millions per day • Tens billions session per year • Online sales 13.2 B per year (http:// fortune.com/2015/11/17/walmart- ecommerce/) • 500B per year sales offline stories (8% USA economy) in ~ 11K stores • The number of transactions ~ 10B (public data)
ML addressable problems • Learning to rank • Given a query, what’s the list of items with the highest probability of conversion (purchase), ATC (add to card), page view
ML addressable problems • Typeahead • Given a sequence of characters types by user, what’s most probably competitions, what are most probable items users wants to buy
ML addressable problems • Spell correction • Given a user query, what’s the query user actually wanted to type
ML addressable problems • Cold start • Given a new items with it’s set of attributes and no history of sales or exposure on site, predict items sales and item sales per query
ML addressable problems • Prediction of LHN • Given a user query, what’s the best set of facet and facet values, which gives higher probability of users interacting with them and finally buying an item
ML addressable problems • Query understanding • Given a query, build a semantic parse of query, tag tokens with attributes: blue tshirts for teenagers -> blue:color tshirts:type for:opt teenagers:agerestriction10-20 • Classification: blue tshirts for teenagers: - > type:apparel, price preference: 10-30, releaseyearpreference: 2014-2016
ML addressable problems • Related searches • Given a query, what are queries which are either semantically close to this one, or represent coincidental users interests • Nike shoes -> adidas shoes, sport shoes, • Coffee mugs -> travel mugs, photo coffee mugs, cappuccino cups
ML addressable problems • product discovery • help users to explore product assortment, • drive users to diverse products • reduce risk of selecting irrelevant items • help to find price,quality,brand etc alternatives • reduce pigeonhole risk • provide relevant data to make a decision
ML addressable problems • Image similarity • Given images of the items, give other items such that images of those are visually appealing to the users which like the original item (appealing by shape? Color? Texture?) -> causing high conversion in recommendation
ML addressable problems • Voice search • Given voice input, reply with a list of the best items • “what are the cheapest samsung tvs in the store” • “what is best deal on queen bed today?”
ML addressable problems • extraction of item attributes • Given an item: what are item attributes: brand, color, size (wheel, screen, height, S/M/XL, Queen/Twin/King/Full), Gender, Pattern, Shape, Features
ML addressable problems • Representations of users : actions on websites/apps -> searches, clicks, browsing behaviour, product -> purchase preferences, reviews, ratings, return rates
ML addressable problems • title generation: how to generate the title which will cause maximum conversion rate • which product attributes select for the title?
What makes a good title?
What makes a good title?
Limits • Most models should be served in production • 50ms on prediction • Part of big system, memory limits ~ 10G
Retail
Retail • Key directions which require machine learning: • discounting tools • coupons and rewards • loyalty • inventory management
Inventory management • Customer want to buy products • Customers have diverse needs • Products should be in stock, ideally in warehouses close to customers • but it’s expensive to store products • Problem: How many products of each type should be stored, when product supply should be refilled?
Questions? • andrei@recruit.ai

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Machine Learning for retail and ecommerce

  • 1. Machine Learning (ML) for eCommerce and Retail Dr. Andrei Lopatenko Director of Engineering, Recruit Institute of Technology Recruit Holdings former Walmart Labs, Google (twice), Apple (twice) andrei@recruit.ai
  • 2. ML for eCommerce • Search, Browse, for commerce sites and application • Help users to find and discover items they will purchase • Maximize revenue/profit per user session
  • 9. Search data size • Catalogue items • 8 M items now compare ~ 400 M Amazon / eBay • X 10 in near future • 2 K text description per item + images • Several hundreds of structured attributes per catalog
  • 10. Search – user searches • Tens of millions per day • Tens billions session per year • Online sales 13.2 B per year (http:// fortune.com/2015/11/17/walmart- ecommerce/) • 500B per year sales offline stories (8% USA economy) in ~ 11K stores • The number of transactions ~ 10B (public data)
  • 11. ML addressable problems • Learning to rank • Given a query, what’s the list of items with the highest probability of conversion (purchase), ATC (add to card), page view
  • 12. ML addressable problems • Typeahead • Given a sequence of characters types by user, what’s most probably competitions, what are most probable items users wants to buy
  • 13. ML addressable problems • Spell correction • Given a user query, what’s the query user actually wanted to type
  • 14. ML addressable problems • Cold start • Given a new items with it’s set of attributes and no history of sales or exposure on site, predict items sales and item sales per query
  • 15. ML addressable problems • Prediction of LHN • Given a user query, what’s the best set of facet and facet values, which gives higher probability of users interacting with them and finally buying an item
  • 16. ML addressable problems • Query understanding • Given a query, build a semantic parse of query, tag tokens with attributes: blue tshirts for teenagers -> blue:color tshirts:type for:opt teenagers:agerestriction10-20 • Classification: blue tshirts for teenagers: - > type:apparel, price preference: 10-30, releaseyearpreference: 2014-2016
  • 17. ML addressable problems • Related searches • Given a query, what are queries which are either semantically close to this one, or represent coincidental users interests • Nike shoes -> adidas shoes, sport shoes, • Coffee mugs -> travel mugs, photo coffee mugs, cappuccino cups
  • 18. ML addressable problems • product discovery • help users to explore product assortment, • drive users to diverse products • reduce risk of selecting irrelevant items • help to find price,quality,brand etc alternatives • reduce pigeonhole risk • provide relevant data to make a decision
  • 19. ML addressable problems • Image similarity • Given images of the items, give other items such that images of those are visually appealing to the users which like the original item (appealing by shape? Color? Texture?) -> causing high conversion in recommendation
  • 20. ML addressable problems • Voice search • Given voice input, reply with a list of the best items • “what are the cheapest samsung tvs in the store” • “what is best deal on queen bed today?”
  • 21. ML addressable problems • extraction of item attributes • Given an item: what are item attributes: brand, color, size (wheel, screen, height, S/M/XL, Queen/Twin/King/Full), Gender, Pattern, Shape, Features
  • 22. ML addressable problems • Representations of users : actions on websites/apps -> searches, clicks, browsing behaviour, product -> purchase preferences, reviews, ratings, return rates
  • 23. ML addressable problems • title generation: how to generate the title which will cause maximum conversion rate • which product attributes select for the title?
  • 24. What makes a good title?
  • 25. What makes a good title?
  • 26. Limits • Most models should be served in production • 50ms on prediction • Part of big system, memory limits ~ 10G
  • 28. Retail • Key directions which require machine learning: • discounting tools • coupons and rewards • loyalty • inventory management
  • 29. Inventory management • Customer want to buy products • Customers have diverse needs • Products should be in stock, ideally in warehouses close to customers • but it’s expensive to store products • Problem: How many products of each type should be stored, when product supply should be refilled?